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Context-aware Emotion Detection from Low-resource Urdu Language Using Deep Neural Network

Published:08 May 2023Publication History
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Abstract

Emotion detection (ED) plays a vital role in determining individual interest in any field. Humans use gestures, facial expressions, and voice pitch and choose words to describe their emotions. Significant work has been done to detect emotions from the textual data in English, French, Chinese, and other high-resource languages. However, emotion classification has not been well studied in low-resource languages (i.e., Urdu) due to the lack of labeled corpora. This article presents a publicly available Urdu Nastalique Emotions Dataset (UNED) of sentences and paragraphs annotated with different emotions and proposes a deep learning (DL)-based technique for classifying emotions in the UNED corpus. Our annotated UNED corpus has six emotions for both paragraphs and sentences. We perform extensive experimentation to evaluate the quality of the corpus and further classify it using machine learning and DL approaches. Experimental results show that the developed DL-based model performs better than generic machine learning approaches with an F1 score of 85% on the UNED sentence-based corpus and 50% on the UNED paragraph-based corpus.

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      cover image ACM Transactions on Asian and Low-Resource Language Information Processing
      ACM Transactions on Asian and Low-Resource Language Information Processing  Volume 22, Issue 5
      May 2023
      653 pages
      ISSN:2375-4699
      EISSN:2375-4702
      DOI:10.1145/3596451
      Issue’s Table of Contents

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      Publication History

      • Published: 8 May 2023
      • Online AM: 1 April 2022
      • Accepted: 23 March 2022
      • Revised: 28 December 2021
      • Received: 24 September 2021
      Published in tallip Volume 22, Issue 5

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